💻 IT & Coding concepts,
clear in 60 seconds.

Availability is about access; reliability is about correct performance.

Database replication helps keep data available and allows systems to handle more read requests.

Kafka uses replication and in-sync replicas to ensure data is always available.

Database replication enhances data availability and read performance but has challenges like replication lag.

Fallbacks allow systems to handle failures safely and maintain user experience.

Dependency failures can disrupt applications, but resilience patterns can help manage them.

Load balancers distribute traffic across multiple servers to enhance performance and reliability.

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

Synchronous replication waits for confirmation from replicas, while asynchronous allows faster writes without waiting.

Stateless servers are easier to scale because they don't store user session data locally.

Cascading failures occur when one service's failure impacts others, causing widespread issues.

Distributed caching spreads data across multiple servers for better performance and reliability.

Partial failure means some services fail while others keep running, impacting system reliability.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

The Bulkhead Pattern isolates resources to protect critical workloads from failures.

Distributed locks ensure only one server performs a task, avoiding duplication.

Alerting helps notify the right people about production issues quickly.

Redis is a fast in-memory data store used for caching and low-latency applications.

Message queues allow services to communicate asynchronously, improving scalability and reliability.

Replicas keep your system available, while snapshots allow for data recovery.

Elasticsearch uses shards for data distribution and replicas for redundancy.

Elasticsearch aliases allow zero-downtime switching between indices for applications.

Consistency models determine what data reads in distributed systems can see.

The USE Method helps find and analyze infrastructure bottlenecks in software systems.

Kafka can help ensure messages are processed once, but requires careful design.

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

Distributed backpressure helps manage the flow of work between services to prevent overload.

Caching helps applications respond faster by serving data from memory instead of hitting the database.

Vertical scaling means upgrading one server, while horizontal scaling means adding more servers.

FAANG engineers use high-level design principles to create scalable systems.

A CDN speeds up web applications by caching content closer to users.

The Cache-Aside Pattern speeds up data retrieval by using a cache to store frequently accessed data.

Choosing the right distributed lock depends on your system's needs and existing tools.

You can update Elasticsearch indices without downtime using alias switching.

Primary shards partition data, while replica shards duplicate it for redundancy.